Search Results (1,421)

The acquisition of nighttime remote-sensing visible-light images is often accompanied by low-illumination effects and haze interference, resulting in significant image quality degradation and greatly affecting subsequent applications. Existing low-light enhancement and dehazing algorithms can handle each problem individually, but their simple cascade cannot effectively address unknown real-world degradations. Therefore, we design a joint processing framework, WFDiff, which fully exploits the advantages of Fourier–wavelet dual-domain features and innovatively integrates the inverse diffusion process through differentiable operators to construct a multi-scale degradation collaborative correction system. Specifically, in the reverse diffusion process, a dual-domain feature interaction module is designed, and the joint probability distribution of the generated image and real data is constrained through differentiable operators: on the one hand, a global frequency-domain prior is established by jointly constraining Fourier amplitude and phase, effectively maintaining the radiometric consistency of the image; on the other hand, wavelets are used to capture high-frequency details and edge structures in the spatial domain to improve the prediction process. On this basis, a cross-overlapping-block adaptive smoothing estimation algorithm is proposed, which achieves dynamic fusion of multi-scale features through a differentiable weighting strategy, effectively solving the problem of restoring images of different sizes and avoiding local inconsistencies. In view of the current lack of remote-sensing data for low-light haze scenarios, we constructed the Hazy-Dark dataset. Physical experiments and ablation experiments show that the proposed method outperforms existing single-task or simple cascade methods in terms of image fidelity, detail recovery capability, and visual naturalness, providing a new paradigm for remote-sensing image processing under coupled degradations. Full article

Most electromagnetic invisibility devices are designed while relying on rigid structures, which have limitations in adapting to complex curved surfaces and dynamic deployment. In contrast, flexible invisibility structures have great application value due to their bendable and easy-to-fit characteristics. In this paper, we propose a flexible metasurface suitable for broadband SAR (Synthetic Aperture Radar) imaging invisibility, which realizes multi-domain joint regulation of electromagnetic waves by designing two subwavelength unit structures with differentiated reflection characteristics and combining array inverse optimization methods. The metasurface employs a sponge-like dielectric substrate and integrates resistive ink to construct a resonant structure, which can suppress electromagnetic scattering through joint phase and amplitude modulation, achieving low detectability of targets in UAV (Unmanned Aerial Vehicle) detection scenarios. Indoor microwave anechoic chamber tests and outdoor UAV-borne SAR experiments verify its stable invisibility performance in a wide frequency band, providing theoretical and experimental support for the application of flexible metasurfaces in dynamic electromagnetic detection countermeasures. Full article

In this study, we consider a fractional-order extension of the Jeffreys equation (also known as the dual-phase-lag equation) by introducing the Reimann–Liouville fractional integral, of order $0<\nu <1$, to the Jeffreys constitutive law, where for $\nu =1$ it corresponds to the conventional Jeffreys equation. The kinetical behaviors of the fractional equation such as non-negativity of the propagator, mean-squared displacement, and the temporal amplitude are investigated. The fractional Langevin equation, or the fractional damped oscillator, is a special case of the considered integrodifferential equation governing the temporal amplitude. When $\nu =0$ and $\nu =1$, the fractional differential equation governing the temporal amplitude has the mathematical structure of the classical linear damped oscillator with different coefficients. The existence of a real solution for the new temporal amplitude is proven by deriving this solution using the complex integration method. Two forms of conditional closed-form solutions for the temporal amplitude are derived in terms of the Mittag–Leffler function. It is found that the proposed generalized fractional damped oscillator equation results in underdamped oscillations in the case of $0<\nu <1$, under certain constraints derived from the non-fractional case. Although the nonfractional case has the form of classical linear damped oscillator, it is not necessary for its solution to have the three common types of oscillations (overdamped, underdamped, and critical damped), unless a certain condition is met on the coefficients. The obtained results could be helpful for analyzing thermal wave behavior in fractals, heterogeneous materials, or porous media since the fractional-order derivatives are related to the porosity of media. Full article

Background/Objectives: Spasticity is a common and disabling sequela of stroke that limits voluntary movement and functional recovery. Vibration therapy (VT) has been proposed as a non-invasive neuromodulatory intervention, but the existing studies report inconsistent outcomes due to methodological heterogeneity. This study aimed to evaluate the overall effectiveness of VT in reducing post-stroke spasticity and to identify optimal stimulation parameters via meta-analytic and meta-regression approaches. Methods: A systematic review and meta-analysis were conducted following the PRISMA 2020 guidelines. Standardized effect sizes (Hedges’ g) were calculated based on the within-group pre–post changes and compared across the groups. Meta-regression and subgroup analyses explored seven potential moderators, including the vibration frequency, amplitude, and time since stroke onset. Results: Thirteen randomized controlled trials (RCTs) involving whole-body or focal vibration interventions in stroke populations were included. Vibration therapy significantly reduced spasticity, yielding a moderate overall effect size (Hedges’ g = −0.50; 95% CI: −0.65 to −0.34; p < 0.001). The greatest treatment effects were observed when VT was applied during the late subacute to early chronic phase (6–12 months post-stroke), with low-frequency (<20 Hz) and low-amplitude (≤0.5 mm) stimulation. The frequency, amplitude, and stroke onset emerged as significant moderators (p < 0.05). Conclusions: Vibration therapy is an effective and clinically meaningful intervention for post-stroke spasticity, particularly when delivered with low-intensity parameters during the optimal recovery window. These findings support the development of individualized VT protocols and provide evidence to guide future rehabilitation strategies. Full article

In this study, a new nonlinear dynamic model was established for functionally graded material (FGM) beams with layered/porous fractal microstructures, aiming to reveal the cross-scale propagation mechanism of flexural waves under large deflection conditions. The characteristics of layered/porous microstructures were equivalently mapped to the fractal dimension index. In the framework of the fractal derivative, a fractal nonlinear wave governing equation integrating geometric nonlinear effects and microstructure characteristics was derived, and the coupling effect of finite deformation and fractal characteristics was clarified. Four groups of deflection gradient traveling wave analytical solutions were obtained by solving the equation through the extended minimal (G′/G) expansion method. Compared with the traditional (G′/G) expansion method, the new method, which is concise and expands the solution space, generates additional csch² soliton solutions and csc² singular-wave solutions. Numerical simulations showed that the spatiotemporal fractal dimension can dynamically modulate the amplitude attenuation, waveform steepness, and phase rotation characteristics of kink solitary waves in beams. At the same time, it was found that the decrease in the spatial fractal dimension will make the deflection curve of the beam more gentle, revealing that the fractal characteristics of the microstructure have an active control effect on the geometric nonlinearity. This model provides theoretical support for the prediction and regulation of the wave behavior of fractal microstructure FGM components, and has application potential in acoustic metamaterial design and engineering vibration control. Full article

To enhance the accuracy and precision of fault diagnosis and location for the collector lines in wind farms under complex operating conditions, an intelligent combined method based on CNN-LSTM and ICEEMDAN-PE-improved wavelet threshold denoising is proposed. A wind power plant model is established using the PSCADV46/EMTDC software. In response to the issue of indistinct fault current signal characteristics under complex fault conditions, a hybrid fault diagnosis model is constructed using CNN-LSTM. The convolutional neural network is utilized to extract the local time-frequency features of the current signals, while the long short-term memory network is employed to capture the dynamic time series patterns of faults. Combined with the improved phase-mode transformation, various types of faults are intelligently classified, effectively resolving the problem of fault feature extraction and achieving a fault diagnosis accuracy rate of 96.5%. To resolve the problem of small fault current amplitudes, low fault traveling wave amplitudes, and difficulty in accurate location due to noise interference in actual wind farms with high-resistance grounding faults, a combined denoising algorithm based on ICEEMDAN-PE-improved wavelet threshold is proposed. This algorithm, through the collaborative optimization of modal decomposition and entropy threshold, significantly improves the signal-to-noise ratio and reduces the root mean square error under simulated conditions with injected Gaussian white noise, stabilizing the fault location error within 0.5%. Extensive simulation results demonstrate that the fault diagnosis and location method proposed in this paper can effectively meet engineering requirements and provide reliable technical support for the intelligent operation and maintenance system of a wind farm. Full article

Robust maritime radar object detection and tracking in maritime clutter environments is critical for maritime safety and security. Conventional Constant False Alarm Rate (CFAR) detectors have limited performance in processing complex-valued radar echoes, especially in complex scenarios where phase information is critical and in the real-time processing of successive echo pulses, while existing deep learning methods usually lack native support for complex-valued data and have inherent shortcomings in real-time compared to conventional methods. To overcome these limitations, we propose a dual-branch sequence feature fusion (DFF) detector designed specifically for complex-valued continuous sea-clutter signals, drawing on commonly used methods in video pattern recognition. The DFF employs dual parallel complex-valued U-Net branches to extract multilevel spatiotemporal features from distance profiles and Doppler features from distance–Doppler spectrograms, preserving the critical phase–amplitude relationship. Subsequently, the sequential feature-extraction module (SFEM) captures the temporal dependence in both feature streams. Next, the Adaptive Weight Learning (AWL) module dynamically fuses these multimodal features by learning modality-specific weights. Finally, the detection module generates the object localisation output. Extensive evaluations on the IPIX and SDRDSP datasets show that DFF performs well. On SDRDSP, DFF achieves 98.76% accuracy and 68.75% in F1 score, which significantly outperforms traditional CFAR methods and state-of-the-art deep learning models in terms of detection accuracy and false alarm rate (FAR). These results validate the effectiveness of DFF for reliable maritime object detection in complex clutter environments through multimodal feature fusion and sequence-dependent modelling. Full article

This work focuses on time and frequency domain analyses of IMU-based orientation estimation algorithms, including indirect Kalman (IKF), Madgwick (MF), and complementary (CF) filters. Euler angles and quaternions are used for orientation representation. A 6-DoF IMU is attached to a 6-joint UR5e robotic arm, with the robot’s orientation serving as the reference. Robotic arm data is obtained via an RTDE interface and IMU data via a CAN bus. Test signals include pose sequences, which are big-amplitude, slowly changing signals used to evaluate stationary and low-dynamics responses in the time domain, and small-amplitude, fast-changing generalized binary noise (GBN) signals used to evaluate dynamic responses in the frequency domain. To prevent poor filters’ performance, their parameters are tuned. In the time domain, RMSE and MaxAE are calculated for roll and pitch. In the frequency domain, composite frequency response and coherence are calculated using the Ockier method. RMSEs are computed for response magnitude and coherence, and averaged equivalent time delay (AETD) is derived from the response phase. In the time domain, MF and CF show the best overall performance. In the frequency domain, they again perform similarly well. IKF consistently performs the worst in both domains but achieves the lowest AETD. Full article

Digital Beamforming SAR (DBF-SAR) provides high-resolution wide-swath imaging capability, yet it is affected by inter-channel amplitude, phase and time-delay errors induced by temperature variations and random error factors. Since all elevation channel data are weighted and summed by the DBF module in real time, conventional record-then-compensate approaches cannot meet real-time processing requirements. To resolve the problem, a real-time calibration architecture for Intermediate Frequency DBF (IFDBF) is presented in this paper. The Field-Programmable Gate Array (FPGA) implementation estimates amplitude errors through simple summation, time-delay errors via a simple counter, and phase errors via single-bin Discrete-Time Fourier Transform (DTFT). The time-delay and phase error information are converted into single-tone frequency components through Dechirp processing. The proposed method deliberately employs a reduced-length DTFT implementation to achieve enhanced delay estimation range adaptability. The method completes calibration within tens of PRIs (under 1 s). The proposed method is analyzed and validated through a spaceborne simulation and X-band 16-channel DBF-SAR experiments. Full article

Recent advances in time-modulated metasurfaces (TMMs) have introduced approaches for controlling target features in radar imaging. These technologies enable dynamic reconstruction of scattering center locations and intensities by flexibly manipulating radar echoes. However, most existing methods focus on amplitude and phase modulation, lacking joint control over the polarimetric scattering characteristics of targets. As a result, the modulated outputs tend to exhibit limited polarimetric diversity and remain strongly tied to the targets’ physical structures. To address this limitation, this paper proposes a modulation method for polarimetric synthetic aperture radar (PolSAR) images based on a flexible metasurface with independently controllable polarizations (FM-ICP). The method independently controls the echo energy distribution in two polarization channels, enabling target representations in PolSAR images to exhibit polarimetric characteristics beyond their physical geometry—for example, rendering a flat plate as a cylinder, or vice versa. In addition, the method can generate synthetic scattering centers with controllable locations and polarimetric properties, which can be precisely tuned via modulation parameters. This work offers a practical approach for target feature manipulation and shows potential in PolSAR image simulation and feature reconstruction. Full article

The thermo-hydrological (TH) coupling model constitutes the foundational framework for investigating the temperature distribution of surrounding rock in cold region tunnels. In this study, a fully coupled TH model is proposed that takes into account multiple physical phenomena during the freezing process of surrounding rock. Firstly, the model was established based on thermodynamics, seepage theory, and ice–water phase change theory, which accounted for unfrozen water, latent heat of phase change, ice impedance, and convective heat transfer. The model was successfully verified by comparing its results to field data. Next, the sensitivity of surrounding rock temperature to environmental, thermodynamic, seepage, and coupling parameters in the fully coupled TH model was systematically studied using a numerical analysis method. The results show that the annual temperature amplitude and thermal conductivity represent the main factors affecting the surrounding rock temperature at a radial depth of 0 m, while the initial temperature and porosity are the key factors at a radial depth of 5 m. Permeability has the least influence on the surrounding rock temperature, but the temperature field will experience sudden changes if its value exceeds its value exceeds 1 × 10⁻¹² m². Finally, using the proposed numerical model, the thickness of insulation layer was simulated, and the degree of influence of the parameters on the thickness of insulation layer was analyzed. This study reveals that the annual temperature amplitude has the greatest influence on the calculation of insulation layer thickness, with its normalized sensitivity factor being approximately 50%. These findings not only expand the methodology for exploring the laws of TH coupling but also provide a theoretical foundation for improving the parameter calibration efficiency and calculation accuracy of the fully coupled TH model, and they have significant reference value. Full article

MnFe₂O₄/Fe soft magnetic composites (SMCs) were designed by the surface oxidation method, and the MnFe₂O₄ layer was utilized as the insulation coating. The microstructure of SMCs and the chemical composition of the insulation layer were observed using scanning electron microscopy and energy-dispersive spectroscopy. The surface phase composition of SMCs was characterized using X-ray diffraction, X-ray photoelectron spectrometry, and Raman spectroscopy. The effect of annealing temperature on the insulation layer was investigated, and its relationship with the magnetic properties of the MnFe₂O₄/Fe SMCs was explored. The best overall performances were obtained at 50 mT and 100 kHz with saturation magnetization M_s = 205 emu/g, amplitude permeability μ_a = 100, and a core loss of 234.9 W/kg. Therefore, this work can provide a method to develop a novel insulating coating to reduce core loss, which is of great significance to the investigation of other Fe-based soft magnetic composites for applications in high-frequency magnetic fields. Full article

Optical interferometry provides high-precision displacement and angle measurement solutions for a wide range of cutting-edge industrial applications. One of the key factors to achieve such precision lies in highly accurate optical encoder signal processing, as well as the calibration and compensation techniques customized for specific measurement principles. Optical interferometric techniques, including laser interferometry and grating interferometry, are usually classified into homodyne and heterodyne systems according to their working principles. In homodyne interferometry, the displacement is determined by analyzing the phase variation of amplitude-modulated signals, and common demodulation methods include error calibration methods and ellipse parameter estimation methods. Heterodyne interferometry obtains displacement information through the phase variation of beat-frequency signals generated by the interference of two light beams with shifted frequencies, and its demodulation techniques include pulse-counting methods, quadrature phase-locked methods, and Kalman filtering. This paper comprehensively reviews the widely used signal processing techniques in optical interferometric measurements over the past two decades and conducts a comparative analysis based on the characteristics of different methods to highlight their respective advantages and limitations. Finally, the hardware platforms commonly used for optical interference signal processing are introduced. Full article

Automatic Modulation Classification (AMC) is vital for adaptive wireless communication, yet it faces challenges in complex environments, including insufficient feature extraction, feature redundancy, and high interclass similarity among modulation schemes. To address these limitations, this paper proposes the Multiscale Complex Convolution Spatiotemporal Attention Network (MCCSAN). In this work, we propose three key innovations tailored for AMC tasks: a multiscale complex convolutional module that directly processes raw I/Q sequences, preserving critical phase and amplitude information while extracting diverse signal features. A spatiotemporal attention mechanism dynamically weights temporal steps and feature channels to suppress redundancy and enhance discriminative feature focus. A combined loss function integrating cross-entropy and center loss improves intraclass compactness and interclass separability. Evaluated on the RML2018.01A dataset and RML2016.10A across SNR levels from −6 dB to 12 dB, MCCSAN achieves a state-of-the-art classification accuracy of 97.03% (peak) and an average accuracy improvement of 3.98% over leading methods. The study confirms that integrating complex-domain processing with spatiotemporal attention significantly enhances AMC performance. Full article

In practical applications, intelligent diagnostic methods for actuator-integrated gearboxes in industrial driving systems encounter challenges such as the scarcity of fault samples and variable operating conditions, which undermine diagnostic accuracy. This paper introduces a multi-layer feature fusion meta-learning (MLFFML) approach to address fault diagnosis problems in cross-condition scenarios with class imbalance. First, meta-training is performed to develop a mature fault diagnosis model on the source domain, obtaining cross-domain meta-knowledge; subsequently, meta-testing is conducted on the target domain, extracting meta-features from limited fault samples and abundant healthy samples to rapidly adjust model parameters. For data augmentation, this paper proposes a frequency-domain weighted mixing (FWM) method that preserves the physical plausibility of signals while enhancing sample diversity. Regarding the feature extractor, this paper integrates shallow and deep features by replacing the first layer of the feature extraction module with a dual-stream wavelet convolution block (DWCB), which transforms actuator vibration or acoustic signals into the time-frequency space to flexibly capture fault characteristics and fuses information from both amplitude and phase aspects; following the convolutional network, an encoder layer of the Transformer network is incorporated, containing multi-head self-attention mechanisms and feedforward neural networks to comprehensively consider dependencies among different channel features, thereby achieving a larger receptive field compared to other methods for actuation system monitoring. Furthermore, this paper experimentally investigates cross-modal scenarios where vibration signals exist in the source domain while only acoustic signals are available in the target domain, specifically validating the approach on industrial actuator assemblies. Full article